Recently, images have increasingly had a high dynamic range (HDR) due to a bit increase of imaging elements (image sensors). The dynamic range of an image can be generally represented by a ratio between minimum luminance and maximum luminance. An HDR image reaches a contrast ratio of 10000:1 or more, for example, between a maximum brightness color and a minimum brightness color, and thus can realistically represent a real world. An HDR image can record all luminance in a visible range, and can support a dynamic range and a color gamut equal to the visual characteristics of humans. An HDR image has advantages of being able to realistically represent shades, simulate exposure, represent glare, and so on.
While content producers capture HDR images as described above, displays in homes to view content are different in performance, including displays supporting standard dynamic range (SDR) with a dynamic range compressed to about one fortieth and displays conversely supporting HDR with a maximum luminance of 500 nit or 1000 nit, for example. Therefore, processing for adapting the dynamic range of original content to the performance of a display at an image output destination (hereinafter, also referred to as “display mapping”) is necessary (see e.g. PTL 1).
However, when conversion of a dynamic range is performed simply by linear scaling in display mapping, a lot of information may be lost, resulting in an image greatly different to the human eye before and after the conversion. Such loss of information is contrary to the intension of a content producer or supplier.